42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
import logging
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from typing import Literal, Optional
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logging.getLogger("transformers").setLevel(logging.WARNING)
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logging.getLogger("sentence_transformers").setLevel(logging.WARNING)
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logging.getLogger("huggingface_hub").setLevel(logging.WARNING)
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from openai import OpenAI
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from sentence_transformers import SentenceTransformer
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from mem0.configs.embeddings.base import BaseEmbedderConfig
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from mem0.embeddings.base import EmbeddingBase
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class HuggingFaceEmbedding(EmbeddingBase):
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def __init__(self, config: Optional[BaseEmbedderConfig] = None):
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super().__init__(config)
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if config.huggingface_base_url:
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self.client = OpenAI(base_url=config.huggingface_base_url)
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else:
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self.config.model = self.config.model or "multi-qa-MiniLM-L6-cos-v1"
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self.model = SentenceTransformer(self.config.model, **self.config.model_kwargs)
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self.config.embedding_dims = self.config.embedding_dims or self.model.get_sentence_embedding_dimension()
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def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
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"""
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Get the embedding for the given text using Hugging Face.
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Args:
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text (str): The text to embed.
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memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
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Returns:
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list: The embedding vector.
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"""
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if self.config.huggingface_base_url:
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return self.client.embeddings.create(input=text, model="tei").data[0].embedding
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else:
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return self.model.encode(text, convert_to_numpy=True).tolist()
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